application of taguchi method in health and safety 2/h45023744.pdf · application of taguchi method...

Alharthi A. A et al Int. Journal of Engineering Research and Applications www.ijera.com

ISSN : 2248-9622, Vol. 4, Issue 5( Version 2), May 2014, pp.37-44

www.ijera.com 37 | P a g e

Application of Taguchi Method In Health And Safety (Fire

Extinguishing Experiment)

Alharthi A. A. & Yang Q. School of Engineering , Brunel University, London

ABSTRACT The traditional Taguchi method is widely used for optimizing the process parameters of a single response

problem. In this paper, Taguchi method is applied to study the effects of five control variables – training,

experience, response to alarm, age, and qualification on extinguishing time and percent damage. An L16

orthogonal array (OA) was used to accommodate the experiment. ANOVA and F-tests and regression are used

to analyze the results. The study indicated that training and experience have the largest effect on the on

extinguishing time and percent damage

Key words: ANOVA analyze, Extinguishing time, Fire extinguishing experiment, orthogonal array (OA),

percent damage and Taguchi method.

I. Introduction Taguchi Method or Robust Engineering,

developed by Genichi Taguchi, is an approach to

Design of Experiments (DOE) for designing products

or processes so that they are robust to environmental

conditions such temperature and humidity. The

objective of Taguchi method is to model responses

(and variance) as a function of controllable (and

uncontrollable) factor levels, then choose levels of

controllable factors to reduce variation transmitted to

the response from variation of the controllable factors

and of the uncontrollable factors, in another word

reduce product variation by choosing levels of the

control factors that dampen the effect of the

uncontrollable or noise factors. Quality is improved

without controlling or removing the cause of

variation, instead, we make the product (or process)

robust to variation in the noise factors [4], [5], [6].

Noise factor is measured by signal to noise ratio and

it is calculated depends on the objective of the

experiment. There are three ways the response could

be optimized[6]:

Objective Signal to noise ratio

Minimize response -10*log(Sy2/n)

(smaller the better)

Maximize the response -10*log(S(1/y2)

/n)

(larger the better)

Nominal is best -10*log(s2)

Multiplied by 10 to put into “deci” “bel” metric, a

terminology used in Electrical Engineering. Taguchi

suggested that “quality” should be thought of, not as

a product being inside or outside of specifications,

but as the variation from the target. He defines

quality as the losses a product imparts to the society

from the time the product is shipped. To quantify

quality loss, write T for the target value and Y for the

measured value [5], [6]. We want E (Y) = T. Write

L(Y) for the loss (in dollars, reputation, customer

satisfaction, ……) for deviation of Y from T. The loss

function is L(Y) = K(Y-T)2

Where, K is some constant. If E (Y) really is T, then

E(L(Y)) = Kσ2 , where σ

2 = Var (Y).

If the product is off target, so that E (Y) = T +d, then

E(L(Y )) = k(σ2+d

2).

Figure 1. Quality loss function

A Taguchi design, or an orthogonal array, is

a method of designing experiments that usually

requires only a fraction of the full factorial

combinations. An orthogonal array means the design

is balanced so that factor levels are weighted equally.

Because of the orthogonality, each factor can be

evaluated independently of all the other factors, so

RESEARCH ARTICLE OPEN ACCESS




the effect of one factor does not influence the

estimation of another factor.

The Steps followed for Taguchi design are:

1. State your problem and objective

2. List responses, control parameters, and sources

of noise

3. Plan the experiment

4. Run experiment and

5. Analyze the experiment and predict improved

parameter settings

II. Problem Identification Fire accidents are common and play an

important role amongst major accidents, not only

because of their relatively high frequency but also

because of their dangerous effects. This makes the

control and protection from the fire accidents a vital

issue that needs to be studied. Our objective is to

determine the effect of employee training,

experience, his or her response to alarm, his or her

age and qualification and their interactions on how

long it takes to extinguish a fire and asses time the

percent damage resulted from the fire.

III. Experimental Details A series of experimental tests were designed

to accomplish these objectives and develop baseline

for future research. The experiment is conducted by

running it at various levels of the factors.

3.1 Determining factors for the study

It was determined the human factors that

need to be studies which influence the performance

of an employee in using the extinguisher are:

a) Training: it is expected that there is difference

between the performance of trained employee

and untrained employee.

b) Experience: the employee with more experience

may perform better in the extinguishing process.

It is worthy to say that the expert employee is

already a trained employee.

c) The response to the alarm: the fast response to

the alarm may lead us to the best scenarios.

d) Age: the employee age directly affects the

physical and mental behavior of the employee in

which it affects his performance.

e) Qualification: higher qualification is predicted to

lead to better performance.

The response variables to be measured and

improved are:

a) Extinguishing time: it is a measure of the

employee performance and measured in seconds.

b) Percentage of damage: it will be used to study

the relation between the extinguishing time and

the damage percentage. It is believed longer

extinguisher time leads to more damage.

3.2 Determining the levels of the factors The Process parameters and their levels used

in the experiment summarized in table 1

Table 1. Factors and their levels

Factors code Level 0 Level 1

Training T Untrained Trained

Experience X without

experience

with

experience

Response to

the alarm

R >30 sec.

(Slow

response)

<30 sec.

(Fast

response)

Age G >40 <40 years

Qualification Q <Bachelor >Bachelor

3.3 Taguchi Design

Taguchi Orthogonal Array L16 design is

used (table 2) which included 5 factors and 16 runs.

Columns of L16(2**15) Array are chosen that are 1,

2, 4, 8 and 15. The L16 is a resolution III design

which means the main effect is confounding with two

factor interaction. The alias structure for L16 is

summarized below:

[A] = A - BC - DE - FG - HJ -KL - MN - OP

[B] = B - AC - DF - EG - HK -JL - MO - NP

[C] = C - AB - DG - EF - HL - JK - MP - NO

[D] = D - AE - BF - CG - HM - JN - KO - LP

[E] = E - AD - BG - CF - HN - JM - KP - LO

[F] = F - AG - BD - CE - HO - JP - KM - LN

[G] = G - AF - BE - CD - HP - JO - KN - LM

[H] = H - AJ - BK - CL - DM - EN - FO - GP

[J] = J - AH - BL - CK - DN - EM - FP - GO

[K] = K - AL - BH - CJ - DO - EP - FM – GN

[L] = L - AK - BJ - CH - DP - EO - FN - GM

[M] = M - AN - BO - CP - DH - EJ - FK – GL

[N] = N - AM -BP - CO - DJ - EH - FL – GK

[O] = O - AP - BM - CN - DK - EL - FH - GJ

[P] = P - AO - BN - CM - DL -EK - FJ - GH

Based on the alias structure, factor C is

confounded with AB interaction, Factor E is

confounded with AD interaction, factor F is

confounded with BD interaction and so on.

The first linear graph for L16 (figure 2)

assisted in matching factors and column and possible

interaction in the experimental matrix [3].

Figure 2. First Linear Graph for L16 Array assigns

the variables and interactions




No replicates of treatment combinations were done,

also the experiment was run in random order to

minimize the effect of extraneous variables that

might influence the results. Alpha (α) or type I error

of 0.05 is used. Alpha is the maximum acceptable

level of risk for rejecting a true null hypothesis.

Run # Factors Extinguishing

Time

Damage

percentage

T X R G Q (seconds) %

1 2 4 8 15

1 0 0 0 0 0 253 84

2 0 0 0 1 1 229 82

3 0 0 1 0 1 219 75

4 0 0 1 1 0 171 73

5 0 1 0 0 1 150 52

6 0 1 0 1 0 125 49

7 0 1 1 0 0 115 18

8 0 1 1 1 1 107 16

9 1 0 0 0 1 160 51

10 1 0 0 1 0 120 26

11 1 0 1 0 0 116 15

12 1 0 1 1 1 96 12

13 1 1 0 0 0 110 20

14 1 1 0 1 1 108 13

15 1 1 1 0 1 96 12

16 1 1 1 1 0 92 8

Table 2 L16 orthogonal array design matrix and the results




3.4 Equipment

Extinguishers: Class A&B extinguishers will

be used. (Foam Extinguisher)

The burned material: Crib of wooden sticks

will be used as a burned material.

Tray containing heptane to light the fire.

Personal protective equipment.

3.5 Experiment Assumptions

The experiments are conducted in the same

conditions, which mean that the place; the burning

material and the extinguishing method are the same

in all the trials. In addition to that, the time for

starting the extinguishing is the same.

3.6 Experiment Conditions

The test room where the experiment was

conducted was closed with the exception of a small

opening at the base of the door (Provided for

ventilation). The wood cribs description is shown in

Table 3.

Table 3: Wood Description

CLASS DIMENSIONS (m)

White Wood 0.5*0.5*0.5

The wood cribs are fixed at 50 cm above

floor level. A properly sized tray is placed beneath

the crib at 30 faraway from the wood cribs (see

Figure 3 and Figure 4). The appropriate heptane

starter charge is poured into the tray. The heptane

charge is ignited and allowed to ignite the wood crib

above [2]. The wood crib is allowed to burn for a

period of 30 seconds before extinguishing (see Figure

5).

For each run, the following steps should be

followed:

Put the wood crib at the center of the

experiment room, and place a tray full of

heptane under it to light the fire.

Ignite the heptane.

Start the extinguishing process after 30

seconds from removing the tray.

Write down the time spent in the

extinguishing process, and the percentage of

damage in the wood for each trial.

IV. Experimental Analysis and Discussion 4.1 Graphical Analysis

4.1.1 Graphical Analysis for Extinguishing Time

The main effects plot for extinguishing time

(figure 6) shows that extinguishing time decreases for

a trained and experienced employee, also it decreases

for a response to alarm for less than 30 seconds,

concluding that training, experience and response to

alarm variables are significant factors while age and

qualification factors are not as significant.

Qualification could be ignored and it may not be used

as factor to improve extinguishing time.




10

168

156

144

132

120

10 10

10

168

156

144

132

120

10

Training

Me

an

Experience Response to Alarm

Age Qualification

Main Effects Plot for Extinguishing TimeData Means

Figure 6. The main effect plot for extinguisher time.

The interactions plot for extinguishing time

(figure 7) shows that there is a significant interaction

between training and experience. The plot indicates

that there is no other interaction exists. We will only

include the Training * Experience interaction as a

term in the statistical analysis.

10 10 10 10

200

150

100

200

150

100

200

150

100

200

150

100

Training

Experience

Response to Alarm

Age

Qualification

0

1

Training

0

1

Experience

0

1

to Alarm

Response

0

1

Age

Interaction Plot for Extinguishing TimeData Means

Figure 7. The interaction plot for extinguisher time

4.1.2 Graphical Analysis for Percent Damage

The main effects plot for the percent damage

(figure 8) shows % damage decreases for a trained

and experienced employee, also it decreases for a

response to alarm for less than 30 seconds,

concluding that training, experience and response to

alarm variables are significant factors while age and

qualification factors are not statistically significant.

10

60

50

40

30

20

10 10

10

60

50

40

30

20

10

Training

Me

an

Experience Response to Alarm

Age Qualification

Main Effects Plot for % DamageData Means

Figure 8. Main Effects Plot for Percent Damage.

The interaction plot (figure 9) shows that

there is a significant interaction between training and

experience, and between age and qualification. We

will include the training * experience and age

qualification interactions as terms in the statistical

analysis.

10 10 10 10

70

45

20

70

45

20

70

45

20

70

45

20

Training

Experience

Response to Alarm

Age

Qualification

0

1

Training

0

1

Experience

0

1

to Alarm

Response

0

1

Age

Interaction Plot for % DamageData Means

Figure 9. Interactions plot for percent damage

4.2 Statistical Analysis

ANOVA or analysis of variance and

multiple regression statistical methods were used to

analyze the data generated by our experiment.

ANOVA is useful for determining the influence of

any giving input parameter from a series of

experimental results for the fire experiment. In

general, ANOVA compares the variation between

groups and the variation within samples by analyzing

their variances. It partitions the total variation into its

appropriate components[1].

Total variance = between groups variance +

variance due to the errors

Where SST = Total Sum of Squares; SSG =

Treatment Sum of Squares between the groups; SSE

= Sum of Squares of Errors. Just think of 'sums of

squares' as being a measure of variation. The method

of measuring this variation is variance, which is

standard deviation squared.

There are 3 assumptions for the ANOVA Statistical

F-test to be valid which involve the εij’s (the error

terms) and are summarized below:

1. The εij’s are normally distributed.

2. The εij’s have mean zero and a common

variance, σ2.

3. The εij’s are independent across

observations.

Similar to Analysis of variance (ANOVA),

multiple linear regression is used to model the

relationship between response variables and one or

more independent variables and variables. The βi are

the regression parameters and ε is an error. The least

square regression method is used for fitting model.

Similar to Analysis of variance (ANOVA), multiple

linear regression is used to model the relationship

between response variables and one or more




independent variables and their relevant interactions

[1]. The model for the multiple regression equation

is: Y = β0 + β1X1 + β2X2 + β3X3 + β4X4 + ………βnXn +

ε

Where y is the response or the dependent variable

and are the independent X1, X2, X3, X4 ...........and Xn

are independent variables. The βi are the regression

parameters and ε is an error. The least square

regression method is used for fitting model.

4.2.1 Statistical Analysis for Extinguishing Time

Statistical outputs for extinguishing time are

summarized in table 4 and 5. Regression analysis

provides the coefficients for the factors and their p-

values and an analysis of variance table. The order of

the coefficients by absolute value indicates the

relative importance of each factor to the response; the

factor with the biggest coefficient has the greatest

impact. The sequential sums of squares in the

analysis of variance table also indicate the relative

importance of each factor; the factor with the biggest

sum of squares has the greatest impact.

Table 4. Regression analysis for Extinguishing

Time .

The regression equation is:

Extinguishing Time = 240 - 95.0 Training - 93.8

Experience - 30.4 Response to Alarm - 21.4 Age +

7.87 Qualification + 72.3 T*X

Predictor Coef SE Coef T P

Constant 239.938 7.725 31.06 0.000

Training -95.000 8.258 -11.50 0.000

Experience -93.750 8.258 -11.35 0.000

Response to Alarm -30.375 5.839 -5.20 0.001

Age -21.375 5.839 -3.66 0.005

Qualification 7.875 5.839 1.35 0.210

T*X 72.25 11.68 6.19 0.000

S = 11.6789 R-Sq = 96.9% R-Sq(adj) = 94.8%

Table 5. Analysis of Variance for Extinguishing

Time

Analysis of Variance

Source DF SS MS F P

Regression 6 38133.9 6355.6 46.60 0.000

Residual Error 9 1227.6 136.4

Total 15 39361.4

Source DF Seq SS

Training 1 13865.1

Experience 1 13282.6

Response to Alarm 1 3690.6

Age 1 1827.6

Qualification 1 248.1

T*X 1 5220.1

The regression analysis shows that the P-

value for Training, Experience, Response to Alarm,

Age, and the Training*Experience interaction are 0,

0, 0.001, 0.005, and 0 respectively which are much

smaller than Alpha of 0.05, indicating statistical

significance, while the P-value for Qualification is

0.21 which is greater than Alpha of 0.05, indicating

non statistical significance.

The prediction equation is:

YTime = 240 – 95XT – 93.8XX – 30.4XR – 21.4XG+

7.87 XQ + 72.3XTXX… (Equation (1))

The residual plot (figure 10) indicates that there is no

violation of the analysis of variance assumptions.

The residuals are normally distributed, the residuals

have equal variances, and the residual are

independent. This concludes that our model is valid.

4.2.2 Statistical Analysis for Percent Damage Statistical outputs for percent damage are

summarized in table 6 and 7. The regression analysis

shows that the P-value for training, experience,

response to alarm, training*experience interaction,

and age*qualification interaction are 0, 0, 0, 0, and

0.004 respectively which are much smaller than

Alpha of 0.05, indicating statistical significance,

while the P-value for age, and qualification are 0.056

and 0.379 respectively which are greater than Alpha

of 0.05, indicating non statistical significance.

Table 6. Regression analysis for % Damage.

The regression equation is

% Damage = 84.1 - 52.5 Training - 44.8 Experience

- 18.5 Response to Alarm + 4.75 Age + 13.3

Qualification + 32.0 T*X - 21.5 G*Q

Predictor Coef SE Coef T P

Constant 84.125 3.793 22.18 0.000

Training -52.500 3.793 -13.84 0.000

Experience -44.750 3.793 -11.80 0.000

Response to Alarm -18.500 2.682 -6.90 0.000

Age 4.750 3.793 1.25 0.246

Qualification 13.250 3.793 3.49 0.008

T*X 32.000 5.365 5.96 0.000

G*Q -21.500 5.365 -4.01 0.004

S = 5.36482 R-Sq = 98.1% R-Sq(adj) = 96.4%

Table 7. Analysis of Variance for % Damage.

Analysis of Variance

Source DF SS MS F P

Regression 7 11659.5 1665.6 57.87 0.000

Residual Error 8 230.2 28.8

Total 15 11889.8

Source DF Seq SS

Training 1 5329.0

Experience 1 3306.3

Response to Alarm 1 1369.0

Age 1 144.0

Qualification 1 25.0

T*X 1 1024.0

G*Q 1 462.3




The prediction equation is:

YDamage =84.1–52.5XT –44.8XX –18.5XR+ 4.75 XG+

13.3 XQ+32XT XX –21.5XGXQ ….. (Equation (3))

The residual plot (figure 11) indicates that

there is no violation of the analysis of variance

assumptions. The residuals are normally distributed,

the residuals have equal variances, and the residuals

are independent. This concludes that our model is

valid.

20100-10-20

99

90

50

10

1

Residual

Pe

rce

nt

25020015010050

20

10

0

-10

-20

Fitted ValueR

esi

du

al

20151050-5-10-15

4.8

3.6

2.4

1.2

0.0

Residual

Fre

qu

en

cy

16151413121110987654321

20

10

0

-10

-20

Observation Order

Re

sid

ua

l

Normal Probability Plot Versus Fits

Histogram Versus Order

Residual Plots for Extinguishing Time

Figure 10. Residual analysis for extinguishing time

1050-5-10

99

90

50

10

1

Residual

Pe

rce

nt

806040200

5

0

-5

-10

Fitted Value

Re

sid

ua

l

5.02.50.0-2.5-5.0-7.5-10.0

4.8

3.6

2.4

1.2

0.0

Residual

Fre

qu

en

cy

16151413121110987654321

5

0

-5

-10

Observation Order

Re

sid

ua

l

Normal Probability Plot Versus Fits

Histogram Versus Order

Residual Plots for % Damage

Figure 11. Residual analysis for % damage model




4.3 Relation between the time of extinguishing

and the damage :

Correlations: Extinguishing Time,% Damage

Pearson correlation of Extiguishing Time and %

Damage = 0.945

P-Value = 0.000

260240220200180160140120100

90

80

70

60

50

40

30

20

10

0

Extiguishing Time

% D

am

ag

e

Scatterplot of % Damage vs Extiguishing Time

Figure 1 : Extinguishing Time vs Damage

Percentage

Figure 12 clarifies the relation between the

time of extinguishing and the damage percentage. It

is obvious that the damage percentage increase with

increasing the time. From the results in the last

section, it is concluded that the training, the

experience , the response to alarm and the interaction

between the training and the experience have the

most influence in decreasing the percentage of

damage, the age has small influence and the

qualification approximately has no influence.

V. Conclusions Our goal is to decrease extinguishing time

and percent damage, the factor levels should be set to

that produce the lowest mean. Examining the main

effects plots and interaction, the factor levels that

decrease extinguishing time and percent damage are

summarized in table below

Factors Level

Training 1

Experience 1

Response to Alarm 1

Age 1

Qualification 1

In conclusion:

a) The training has the highest effect in regard of

the performance of the employees in fire

extinguishing.

b) The experience factor is ranked in the second

stage according to its effect in the performance

of the employee in fire extinguishing.

c) The interaction between the training and the

experience was also a significant factor.

d) The age has small affect in the performance of

the employees in fire extinguishing.

e) The qualification’s effect on the performance of

the employees in fire extinguishing can also be

neglected.

A trained employee, less than 40 years of

age, with a bachelor degree and experience, and with

fast response leads to the best result in extinguishing

time and percent damage.

References [1] Bala MG, Biswanath M & Sukamal G,

Taguchi Method and ANOVA: An approach

for process parameters optimization of hard

machining while machining hardened steel,

Journal of Scientific & Industrial Research,

Vol. 68, August 2009, pp. 686-695

[2] Carey, W.M., National Class A Foam

Research Project Technical Report:

Knockdown, exposure and Retention Tests.

National Fire Protection Research

Foundation,1993

[3] Local Government Group (2011) Fire safety

in purpose-built blocks of flats,

http://www.local.gov.uk/c/document_library

/get_file?uuid=71e152a6-9e0a-4810-aee6-

498167664f79&groupId=10171

[4] Peace, G. S., Taguchi Methods: a Hands-on

Approach, Addison-Wesley Publishing

Company, Inc., Massachusetts, 1992.

[5] Ross, R. J., Taguchi Techniques for Quality

Engineering, McGraw-Hill, New York,

1989.

[6] Under writers Laboratories Inc., Report of

Class A Foam Tests, Department of the

Army, Fort Belvoir, 1994.

http://www.local.gov.uk/c/document_library/get_file?uuid=71e152a6-9e0a-4810-aee6-498167664f79&groupId=10171



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